A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine L...

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Detalles Bibliográficos
Autores principales: Sharma, Ayushi, Bhardwaj, Harshit, Bhardwaj, Arpit, Sakalle, Aditi, Acharya, Divya, Ibrahim, Wubshet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307347/
https://www.ncbi.nlm.nih.gov/pubmed/35875749
http://dx.doi.org/10.1155/2022/9869948
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author Sharma, Ayushi
Bhardwaj, Harshit
Bhardwaj, Arpit
Sakalle, Aditi
Acharya, Divya
Ibrahim, Wubshet
author_facet Sharma, Ayushi
Bhardwaj, Harshit
Bhardwaj, Arpit
Sakalle, Aditi
Acharya, Divya
Ibrahim, Wubshet
author_sort Sharma, Ayushi
collection PubMed
description Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers. Also, we show the classification accuracy comparison between them. The results showed that the CNN classifier achieved the highest classification accuracy of 98.83%.
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spelling pubmed-93073472022-07-23 A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits Sharma, Ayushi Bhardwaj, Harshit Bhardwaj, Arpit Sakalle, Aditi Acharya, Divya Ibrahim, Wubshet Comput Intell Neurosci Research Article Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers. Also, we show the classification accuracy comparison between them. The results showed that the CNN classifier achieved the highest classification accuracy of 98.83%. Hindawi 2022-07-15 /pmc/articles/PMC9307347/ /pubmed/35875749 http://dx.doi.org/10.1155/2022/9869948 Text en Copyright © 2022 Ayushi Sharma et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Sharma, Ayushi
Bhardwaj, Harshit
Bhardwaj, Arpit
Sakalle, Aditi
Acharya, Divya
Ibrahim, Wubshet
A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
title A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
title_full A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
title_fullStr A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
title_full_unstemmed A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
title_short A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits
title_sort machine learning and deep learning approach for recognizing handwritten digits
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9307347/
https://www.ncbi.nlm.nih.gov/pubmed/35875749
http://dx.doi.org/10.1155/2022/9869948
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